Abstract

Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference and evidence synthesis of two sample data sets. Thus, parameter uncertainty was represented by a joint posterior distribution, which we then used to predict the risk and to evaluate the criteria for acceptance of production batches. We also applied the Bayesian model to compare alternative criteria, accounting for the statistical uncertainty of parameters, conditional on the data sets. Comparison of the posterior mean relative risk, E(RR vertical bar data) = E(P(illness vertical bar criterion is met)/ P (illness)vertical bar data), and relative posterior risk, RPR = P(illness vertical bar data, criterion is met)/ P (illness vertical bar data), showed very similar results, but computing is more efficient for RPR. Based on the sample data, together with the QMRA model, one could achieve a relative risk of 0.4 by insisting that the default criterion be fulfilled for acceptance of each batch.

Keywords

Bayesian modeling; hierarchical models; evidence synthesis; uncertainty; OpenBUGS; 2D Monte Carlo; quantitative microbiological risk assessment; food safety; Campylobacter

Published in

Annals of Applied Statistics
2015, Volume: 9, number: 3, pages: 1415-1432
Publisher: INST MATHEMATICAL STATISTICS

SLU Authors

• Lindqvist, Roland

  • Department of Molecular Sciences, Swedish University of Agricultural Sciences
    
  • National Food Agency

Sustainable Development Goals

SDG2 End hunger, achieve food security and improved nutrition and promote sustainable agriculture


UKÄ Subject classification

Microbiology


Publication identifier

DOI: https://doi.org/10.1214/15-AOAS845

Permanent link to this page (URI)

https://res.slu.se/id/publ/73398